A plurality of problems are usually encountered when dealing with biologically realistic connectionist networks. First, in case connections are not learned statistically, they have to be defined holistically as connecting two points in some high-dimensional neuronal feature space, which is an almost impossible task. Second, due the high dimensionality it is difficult to interpret and visualize the internal states of a network during simulations. Furthermore, the simulation (both memory and performance) of such networks are mainly determined by the number of connections.
The present invention particularly targets at convolutional networks, i.e. having layers with sub-areas, wherein neurons of a sub-area of a layer of the network share a common connectivity weight matrix. The main problems encountered with convolutional networks are the extensive calculation of the dynamics using the shared connections, the non-intuitive design of the connection patterns and the difficult handling of the results. Local dynamic normalization procedures further increase the computational overhead. Finally, a visualization of the network state (i.e. its activity) occurs in a multi-dimensional space.